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Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. (1402). Differential Protection of ISPST Using Chebyshev Neural Network ‎. سامانه مدیریت نشریات علمی, 11(2), 123-129. doi: 10.22098/joape.2023.10004.1709
S. K. Bhasker; M. Tripathy; A. Agrawal; A. Mishra. "Differential Protection of ISPST Using Chebyshev Neural Network ‎". سامانه مدیریت نشریات علمی, 11, 2, 1402, 123-129. doi: 10.22098/joape.2023.10004.1709
Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. (1402). 'Differential Protection of ISPST Using Chebyshev Neural Network ‎', سامانه مدیریت نشریات علمی, 11(2), pp. 123-129. doi: 10.22098/joape.2023.10004.1709
Bhasker, S. K., Tripathy, M., Agrawal, A., Mishra, A.. Differential Protection of ISPST Using Chebyshev Neural Network ‎. سامانه مدیریت نشریات علمی, 1402; 11(2): 123-129. doi: 10.22098/joape.2023.10004.1709

Differential Protection of ISPST Using Chebyshev Neural Network ‎

Journal of Operation and Automation in Power Engineering
دوره 11، شماره 2، آبان 2023، صفحه 123-129  XML اصل مقاله (746.59 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10004.1709
نویسندگان
S. K. Bhasker email 1؛ M. Tripathy2؛ A. Agrawal3؛ A. Mishra3
1Department of Electrical Engineering, Faculty of Engineering & Technology, University of Lucknow, Lucknow, India
2Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India‎
3Department of Electronics and Communication Engineering, BML Munjal University, Haryana, India‎
چکیده
An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters.
کلیدواژه‌ها
Energization؛ Internal Fault؛ Chebyshev Neural Network (ChNN)؛ ISPST؛ PSCAD/EMTDC.‎
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